StaR Child Health: An initiative that seeks to improve the quality of design, conduct, and reporting of pediatric clinical research by promoting the use of modern research standards. www.starchildhealth.org
Maternal Infant Child & Youth Research Network (MICYRN): A collaborative national initiative to build capacit for high quality clinical research in Canada. MICYRN links 17 participating academic health centres and hundreds of investigation teams across the country. www.micyrn.ca
EQUATOR Network: An international initiative that seeks to improve reliability and value of medical research literature by promoting transparent and accurate reporting of research studies. www.equator-network.org
Creating random numbers: A number of stats programs will allow you to generate random numbers. When using this technique, it is necessary to have an a priori classification system that will be used to assign individuals to the different treatment groups. For example, using a program like Excel will allow you to generate random numbers between 0 and 1, and you may decide that any individuals with numbers <0.5 will be assigned to Group A and any individuals with numbers >=0.5 will be assigned to Group B.
The syntax for various programs is provided below:
Microsoft Excel: =rand()
Stata: gen x = runiform()
R: random.seed
runif(n, min=0, max=1)
Randomization services: There are also online services that can create your randomization sequence. Some examples are provided below.
Appropriate means of blinding study participants, personnel, and outcome assessors:
No blinding or incomplete blinding, but the outcome is not likely to be influenced by lack of blinding
Blinding of key personnel ensured, and unlikely that blinding could have been broken
Practical tips for blinding surgical trials: Karanicolas et al. 2010
Recommendations for blinding behavioural interventions: Friedberg et al. 2010 [PubMed]
Reasons for missing outcome data unlikely to be related to true outcome (for survival data, censoring unlikely to be introducing bias)
Missing outcome data balanced in numbers across intervention groups, with similar reasons for missing data across groups
For dichotomous outcome data, the proportion of missing outcomes compared with observed event risk not enough to have a clinically relevant impact on the intervention effect estimate
For continuous outcome data, plausible effect size (difference in means or standardized difference in means) among missing outcomes not enough to have a clinically relevant impact on observed effect size
Missing data have been imputed using appropriate methods
CONSORT flow diagram: The CONSORT flow diagram outlines the flow of study participants through the stages of an RCT. This information can then be used to assess whether an intention-to-treat analysis has been conducted.
Imputation: Imputation is the substitution of some value for missing data. There are many different methods of imputing data, but all are associated with pros and cons, and there is no technique that is the best for all situations. Some guidance for deciding which method to use can be found at: www.missingdata.org.uk. Techniques include:
Logic: missing value is deduced from edit rules
Mean: missing value is replaced by the mean of the respondents
Ratio: missing value is replaced by the adjusted value of another variable
Previous value (last observation carried forward): missing value is replaced by the value declared at the previous occasion
Unit trend: missing value is replaced by the value declared at the previous occasion, but adjusted according to the trend of the unit
Group trend: missing value is replaced by the value declared at the previous occasion, but adjusted according to a group trend
Regression: missing value is replaced by other variables' adjusted values
Imputation using a model: missing value is replaced by a value predicted using a model adjusted on the respondents
Hot-deck: missing value is replaced by a randomly chosen value from the respondents in the current file
Cold-deck: missing value is replaced by a randomly chosen value from the respondents in another file
Nearest neighbour: missing value is replaced by the nearest neighbour's value, according to a distance function based on one or more auxiliary variable
Imputation with residuals: missing value is replaced by a predicted value to which a randomly selected residual is added
Imputation with forced residuals: missing value is replaced by a predicted value to which a randomly selected residual is added but subject to constraints
Probability: in the case of (0,1) variables, the missing value is replaced by the probability of obtaining a value of 1
Nearest neighbour's trend: missing value is replaced by the value reported at a previous occasion modified according to the trend of the nearest neighbour
Nearest predicted value: missing value is replaced by the value which is nearest to the value predicted for the nonrespondent (hybrid method between model and donor imputation)
Logistic imputation followed by model imputation: logistic regression is used to determine the category and the missing value is replaced by a value predicted using a model adjusted on the respondents
The study protocol is available and all of the study’s pre-specified (primary and secondary) outcomes that are of interest in the review have been reported in the pre-specified way
The study protocol is not available but it is clear that the published reports include all expected outcomes, including those that were pre-specified
Trial Registration: Since July 1, 2005, the International Committee of Medical Journal Editors (ICMJE) has endorsed prospective trial registration (ICMJE Statement). This includes registering all trials with a publicly available registry prior to participant enrollment. Registries accepted by the ICMJE include:
Australian New Zealand Clinical Trials Registry (ANZCTR)
The WHO maintains the International Clinical Trials Registry Platform (ICTRP), a searchable database of multiple registries. It also provides a useful overview of trial registration, including:
The registry can be searched at: http://apps.who.int/trialsearch/. A pediatric search filter can be applied by using the "Advanced Search" option and checking the box that says "Search for clinical trials in children."
General
Table of Contents
Download:
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Sequence generation
Appropriate means of generating a random sequence:
Random number table: The WHO gives an example of a random number table and instructions for its use.
Creating random numbers: A number of stats programs will allow you to generate random numbers. When using this technique, it is necessary to have an a priori classification system that will be used to assign individuals to the different treatment groups. For example, using a program like Excel will allow you to generate random numbers between 0 and 1, and you may decide that any individuals with numbers <0.5 will be assigned to Group A and any individuals with numbers >=0.5 will be assigned to Group B.
The syntax for various programs is provided below:
Randomization services: There are also online services that can create your randomization sequence. Some examples are provided below.
Clinical trials coordinating centres may also offer randomization services.
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Allocation concealment
Appropriate means of concealing group allocation:
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Blinding
Appropriate means of blinding study participants, personnel, and outcome assessors:Practical tips for blinding surgical trials: Karanicolas et al. 2010
Recommendations for blinding behavioural interventions: Friedberg et al. 2010 [PubMed]
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Incomplete outcome data
Appropriate handling of outcome data:
CONSORT flow diagram: The CONSORT flow diagram outlines the flow of study participants through the stages of an RCT. This information can then be used to assess whether an intention-to-treat analysis has been conducted.
Imputation: Imputation is the substitution of some value for missing data. There are many different methods of imputing data, but all are associated with pros and cons, and there is no technique that is the best for all situations. Some guidance for deciding which method to use can be found at: www.missingdata.org.uk. Techniques include:
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Selective outcome reporting
Appropriate means of reporting outcomes:
Outcome measure planning tool
Trial registration resources (see below)
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Other sources of bias
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Trial registration
Trial Registration: Since July 1, 2005, the International Committee of Medical Journal Editors (ICMJE) has endorsed prospective trial registration (ICMJE Statement). This includes registering all trials with a publicly available registry prior to participant enrollment. Registries accepted by the ICMJE include:The WHO maintains the International Clinical Trials Registry Platform (ICTRP), a searchable database of multiple registries. It also provides a useful overview of trial registration, including:
1) Why is trial registration important?
2) How to register a trial
3) Organizations with policies
The registry can be searched at: http://apps.who.int/trialsearch/. A pediatric search filter can be applied by using the "Advanced Search" option and checking the box that says "Search for clinical trials in children."
[back to top]